Tsunami data assimilation for early warning
著者
書誌事項
Tsunami data assimilation for early warning
（Springer theses : recognizing outstanding Ph. D. research）
Springer nature Singapore, 2022
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注記
Doctoral thesis accepted by The University of Tokyo, Tokyo, Japan
内容説明・目次
内容説明
This book focuses on proposing a tsunami early warning system using data assimilation of offshore data. First, Green's Function-based Tsunami Data Assimilation (GFTDA) is proposed to reduce the computation time for assimilation. It can forecast the waveform at Points of Interest (PoIs) by superposing Green's functions between observational stations and PoIs. GFTDA achieves an equivalently high accuracy of tsunami forecasting to the previous approaches, while saving sufficient time to achieve an early warning. Second, a modified tsunami data assimilation method is explored for regions with a sparse observation network. The method uses interpolated waveforms at virtual stations to construct the complete wavefront for tsunami propagation. Its application to the 2009 Dusky Sound, New Zealand earthquake, and the 2015 Illapel earthquake revealed that adopting virtual stations greatly improved the tsunami forecasting accuracy for regions without a dense observation network. Finally, a real-time tsunami detection algorithm using Ensemble Empirical Mode Decomposition (EEMD) is presented. The tsunami signals of the offshore bottom pressure gauge can be automatically separated from the tidal components, seismic waves, and background noise. The algorithm could detect tsunami arrival with a short detection delay and accurately characterize the tsunami amplitude. Furthermore, the tsunami data assimilation approach is combined with the real-time tsunami detection algorithm, which is applied to the tsunami of the 2016 Fukushima earthquake. The proposed tsunami data assimilation approach can be put into practice with the help of the real-time tsunami detection algorithm.
目次
Chapter 1 Introduction
1.1 Tsunami Early Warning
1.2 Numerical Modeling of Tsunami Propagation
1.3 Tsunami Data Assimilation Approach
1.4 Network of Offshore Bottom Pressure Gauges
1.5 Real-time Tsunami Detection
1.6 Objectives
Chapter 2 Green's Function-based Tsunami Data Assimilation (GFTDA)
2.1 Principles of GFTDA
2.2 Assimilation Process and Mathematical Equivalence
2.3 Validation Test-2012 Haida Gwaii Earthquake
2.4 Adoption of Linear Dispersive Model-2004 off the Kii Peninsula Earthquake
2.5 Application to Real-time Data-2015 Torishima Volcanic Tsunami Earthquake
2.6 Discussion
Chapter 3 Tsunami Data Assimilation with Interpolated Virtual Stations
3.1 Linear Interpolation with Huygens-Fresnel Principle
3.2 Test with Synthetic Data-2004 Sumatra-Andaman Earthquake
3.3 Test with Real Data-2009 Dusky Sound Earthquake
3.4 Application to Far-field Event-2015 Illapel Earthquake
3.5 Discussion
Chapter 4 Real-Time Tsunami Detection based on Ensemble Empirical Mode Decomposition (EEMD)
4.1 EEMD
4.2 Validation Test-2016 Fukushima Earthquake
4.3 Discussion
Chapter 5 Real-time Tsunami Data Assimilation of S-net Pressure Gauge Records during the 2016 Fukushima Earthquake
5.1 Introduction
5.2 Data and Methods
5.3 Results
5.4 Discussion
Chapter 6 Tsunami Early Warning System Using Data Assimilation of Offshore Data
6.1 Practical Implementation
6.2 Future Improvements
Chapter 7 Summary
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